The popularity of imaging consumer devices such as digital still cameras, imaging phones and the like and of digital image processing techniques have led to a proliferation of new enhancement processes of color images both in global or semantic terms, as discussed, for example, in S. Battiato, A. Castorina, M. Guarnera, P. Vivirito “A Global Enhancement Pipeline for Low-cost Imaging Devices”, IEEE Transactions on Consumer Electronics, Vol. 49, Issue 3, pp. 670-675, August 2003 and/or S. Battiato, A. Bosco, A. Castorina, G. Messina “Automatic Global Image Enhancement by Skin Dependent Exposure Correction” Proc. of IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, NSIP 2003, Grado, Italy, June 2003.
For still pictures of natural scenes, landscapes, portraits etc., a widely accepted assumption is that colors related to a small number of classes have a major perceptive impact on the human visual system. This is discussed, for example, in S. N. Yendrikhovskij, F. J. J. Blommaert, H. de Ridder, “Optimizing color reproduction of natural images”, Proc. Of Sixth Color Imaging Conference: Color Science, Systems and Applications, pp. 140-145, 1998; E. J. Lee, Y. H. Ha, “Favorite Color Correction for Favorite Colors”, IEEE Trans. On Consumer Electronics, vol. 44, No. 1, pp. 10-15, February 1998; and U.S. Pat. No. 6,738,510, “Image Processing Apparatus”, 2001.
Studies show that basic chromatic classes are essentially: skin (complexion), vegetation, sky/sea. These classes seem to have a predominant perceptive impact on the human visual system. Classical global techniques (histogram equalization, contrast enhancement) work in an unsupervised way, that is without taking into account specific peculiarities of each of the color classes of an image to be processed, as discussed in R. C. Gonzalez, R. E. Woods, “Digital Image Processing”, Addison Wesley, 1993.
Other approaches of color correction are known. For example, PaintShopPro8: http://www.jasc.com, Jasc Software Inc. or ICorrect: http://www.picto.com, Pictographics Inc., allow an automatic color enhancement. The first technique allows to perform an automatic saturation enhancement, by correcting the whole image in the same way without implementing any adaptive control. By contrast, the second technique performs a manually driven color correction, in a semi-automatic way. The user must specify the color targets for real classes and then a global correction is performed on the whole image, often producing unpleasant color cast artifacts. In both cases a global correction is performed on the whole image.
The U.S. Pat. No. 6,738,510 describes a possible approach for a color class guided correction. Other patents, such as U.S. Pat. No. 6,721,000, “Adaptive Pixel-level color enhancement for a digital camera”, Apr. 13, 2004, U.S. Pat. No. 6,611,618, “Wide-Band Image Enhancement”, Aug. 26, 2003, and/or U.S. Pat. No. 6,081,653, “Color Imaging”, Jun. 27, 2000 describe techniques of color correction in a general or adaptive way.
The techniques for image correction/improvement according to an adaptive approach, even if potentially effective, so far are not widely used because of the intrinsic difficulties of adequately filtering the large amount of statistical data that may be gathered from an image to be processed containing many details of different colors without requiring a burdensome amount of computing resources.